Short Bio

Dr. Igor Jurisica, PhD, DrSc is a Senior Scientist at Krembil Research Institute, Professor at U Toronto and Visiting Scientist at IBM CAS. He is also an Adjunct Professor at the School of Computing, Pathology and Molecular Medicine at Queen's U, Computer Science at York U, an adjucnct scientist at the Institute of Neuroimmunology, Slovak Academy of Sciences and an Honorary Professor at Shanghai Jiao Tong University. Since 2015, he has also served as Chief Scientist at the Creative Destruction Lab, Rotman School of Management.

Dr. Jurisica has won numerous awards, including a Tier I Canada Research Chair in Integrative Cancer Informatics, the IBM Faculty Partnership Award (3-time recipient), and IBM Shared University Research Award (4-time recipient). He has been included in Thomson Reuters 2016, 2015 & 2014 list of Highly Cited Researchers, and The World's Most Influential Scientific Minds: 2015 & 2014 Reports. In 2019 he was included in the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare list.

His research focuses on integrative informatics and the representation, analysis and visualization of high-dimensional data to identify prognostic/predictive signatures, determine clinically relevant combination therapies, and develop accurate models of drug mechanism of action and disease-altered signaling cascades.

Research/Teaching

Research Synopsis

The primary research focus is on integrative computational biology, and representation, analysis and visualization of high dimensional data generated by high-throughput biology experiments. Of particular interest is the use of comparative analysis in the mining of different dataset types such as protein-protein interaction, gene/protein expression profiling, microRNA:target, drug:target, and high-throughput screens for protein crystallization.

Intelligent molecular medicine Technologies to measure gene, protein and microRNA activity offer the opportunity to improve our understanding of tumourigenesis and patient treatment. However, molecular profiling alone is not sufficient to achieve intelligent molecular medicine. Computational advances and computing power to analyze, manage and use genomic/proteomic information combined with information about drugs, their targets and mode of action are required to turn data into knowledge for hypotheses generation for further research or to render them readily comprehensible for patient outcome prediction and treatment selection. Our focus is on algorithm and tools development, their application and evaluation.

Many techniques for the analysis of genomic/proteomic data are available, yet none offers an integrated and comprehensive approach, by combining results from gene/protein expression data in the context of protein-protein interactions. We address this bottleneck in multiple cancers by systematic, unbiased analysis and visualization of data integrated from multiple high-throughput platforms under the hypothesis that such information will create insight not appreciable from the component parts.

The results of this research will help to fathom biological mechanisms of cancer, and will be applicable to improve disease classification, diagnostic measures, therapy planning and treatment prognosis. Improving the treatment could in turn improve quality of life for cancer patients. Using the proposed tools and methodology, physicians will have more relevant information available at the time of diagnosis and treatment planning, and the patient will have a better explanation of the disease, its origin, progression path and treatment alternatives.

Structure-function relationship in protein interaction networks It has been established that despite inherent noise present in protein-protein interaction (PPI) data sets, systematic analysis of resulting networks uncovers biologically relevant information, such as lethality, functional organization, hierarchical structure and network-building motifs. These results suggest that PPI networks have strong structure-function relationships. We are developing novel graph theory based algorithms for systematic analysis of PPI networks (both predicted and experimentally determined). We use this information to build predictive models and to integrate this information with gene/protein expression profiles.

High-throughput protein crystallizationOne of the fundamental challenges in modern molecular biology is the elucidation and understanding of the rules by which proteins adopt their three-dimensional structure. Currently, the most powerful method for protein structure determination is single crystal X-ray diffraction, although new breakthroughs in NMR and in silico approaches are growing in their importance.

Conceptually, protein crystallization can be divided into two phases: search and optimization. Approximate crystallization conditions are identified during the search phase, while the optimization phase varies these conditions to ultimately yield high quality crystals. Robotic protein crystallization screening can speedup the search phase, and has a potential to increase process quality. However, this requires an automated process for evaluating experiment results. We focus on automated image classification, data mining of resulting information when integrated with protein properties, using the information for crystallization optimization planning and screen optimization.